Kai Schulz

🎯 𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 > 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 (10 and final) 𝗧𝗵𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗠𝗶𝗿𝗮𝗴𝗲 Everyone is excited about “autonomous marketing.” LLMs writing copy. Agents orchestrating journeys. AI deciding what every customer sees. It feels like the future. Most of it is optimizing inside a hallucination. I call it The Optimization Mirage. 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗺𝗶𝘀𝘁𝗮𝗸𝗲: 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗻𝗴 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻Churn scores. LTV models. Propensity rankings. These are descriptive models. They contain zero intelligence about what will happen if you intervene. In lifecycle marketing, their role must be narrow: • As surrogate outcomes for experimentation when true LTV is delayed • As context features inside a real decisioning engine They are inputs. They cannot be policies. Using descriptive models as decision engines is like trying to fly a plane by reading yesterday’s weather report. 𝗧𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗺𝗶𝘀𝘁𝗮𝗸𝗲: 𝗹𝗲𝘁𝘁𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 “𝗱𝗲𝗰𝗶𝗱𝗲” The natural extension! An LLM can: • Generate treatments • Embed context • Summarize history It can tell you what usually happens together. But it cannot reason about why a treatment caused which outcome. Correlation does not translate to policy. 𝗧𝗵𝗲 𝘁𝗵𝗶𝗿𝗱 𝗺𝗶𝘀𝘁𝗮𝗸𝗲: 𝗧𝗵𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻 𝗧𝗿𝗮𝗽 Even the most state-of-the-art reinforcement learning systems must initialize somewhere. If you let an LLM initialize treatments based on correlational patterns in your warehouse, you fall into: The Horizon Trap. It looks like intelligence because it reflects past patterns. But those patterns are not causal. They are status-quo artifacts. The decisioning will converge. But to what? A local maximum defined by correlational guesses. You can dynamically allocate traffic across five bad ideas and still lose money. The algorithm didn’t fail. Your initialization horizon was wrong. 𝗪𝗵𝗮𝘁 𝗿𝗲𝗮𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 If you want to avoid: • Boomerang effects • Slow convergence • Local maxima traps You need causal priors, and decisioning belongs in constrained, auditable, outcome-driven systems. Have you seen LLM-driven “autonomous” systems produce real incremental lift — or just elegant automation?

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A plea for data literacy education! 🧑‍🏫 In this interview, Dr. Joe Perez explains why companies can no longer avoid investing in their employees’ data skills and outlines how to approach this effectively. 👉 https://lnkd.in/eEAxPpJM #tgoa #thegroupofanalysts #knowledgeworkers #dataliteracy

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McKinsey now has 25,000 AI agents, which is more than the population of many towns. Most of these agents were added in less than two years. AI is not just on its way to consulting; it is already here, changing how work is done and who does it. This matters because it shows what the next era of professional services will look like: faster, more streamlined, and focused on people working with agents instead of against them. Here are 5 key points that stood out: 1* McKinsey now has 60,000 workers in total, with 25,000 of them being AI agents. 2* The company plans for every employee to be paired with at least one agent within the next 18 months. 3* Work driven by AI already accounts for 40% of McKinsey’s business, thanks to QuantumBlack. 4* The company now looks for people who can think both like consultants and engineers. 4* The business model is shifting from giving advice and making slide decks to focusing on shared results and AI-driven change. This is a massive shift. It turns consulting into something closer to: A transformation partner, A systems integrator, A co‑owner of outcomes, and A builder/operator of AI workflows! It also means AI agents aren’t just internal productivity tools, they’re part of the product McKinsey sells!! Here's My take: This is not about replacing people. It is about removing the slow, manual parts of the job that used to hold teams back. The firms that succeed will treat AI agents as real teammates and help their people work at a higher level. Talent will be measured less by background and more by adaptability, curiosity, and the ability to work well with intelligent systems. This is the future happening right now. My advice, as always, is to learn to use AI now, don't wait until tomorrow, it might be too late then!!! #AITransformation #FutureOfWork #ConsultingIndustry #AIAgents #McKinsey #QuantumBlack #DigitalConsulting #WorkforceInnovation #AIInBusiness #ProfessionalServices #TechStrategy #BusinessTransformation #AIProductivity #HumanAndAI #ConsultingCareers #AutomationTrends #AIAdoption #EnterpriseAI #LeadershipInsights #FutureSkills #LearnAI #LearnToUseAI #AITransformation #AIAdaption #AIFirst #BCG #AIEnabled  https://lnkd.in/eeUapHgB

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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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GPT-5 is here. What does that mean for the average market researcher? Many of the key improvements OpenAI has made with GPT-5 are upgrades to usability, so if you haven't gotten very far on your AI journey yet, there's never been a better time to jump in. But where do you start? Here are three things that will pay immediate dividends. 1. Learn the ChatGPT Platform Here are some things you can do within the ChatGPT platform: attach documents and spreadsheets, have ChatGPT generate in depth research reports on a specific topic with Deep Research, output documents and spreadsheets, design CustomGPTs to encode portions of your workflow and thinking, and many more. If you don't know what these things are, where they live in the platform, or how to use them, then there's a lot more that ChatGPT could be helping you with. 2. Test Your Assumptions about What AI Can Do Previously, the default model for most users of ChatGPT was not the best model, so unless you knew where to select your model and which of the confusingly named models was the most powerful (o3 is better than 4o...is that not obvious??), you may not know what AI is actually capable of. With dynamic switching between models now handled automatically, and a more powerful model underpinning everything, you'll get a much better idea of where and how AI can actually help you if you go and actually try out three or four representative tasks. 3. Make Your Requirements Explicit AI can't read your mind. While ChatGPT continues to get better and better at executing the technical aspects of a huge range of tasks, it still doesn't know certain critical details, like the unspoken priorities of the client, the norms in your company, or your sense of taste. If you want AI to take these things into account when generating outputs for you, you need to tell it. Communicating these things is not easy for most people, so when you're testing out the new model, give some thought to the gap between what you're assuming is obvious and what the model couldn't possibly know without you telling it, and start trying to articulate it. AI is, and will continue to get better. But it's a tool, and it's up to you to make sure that you learn how to use it effectively. Dig Insights

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AI Replaces Humans? The uncomfortable truth. Data & Analytics roles are expensive. A Senior Product Analyst costs $165,000. Our AI Agent can do the same job for $5,800. That's $159,200 in savings. 28.4x ROI. AI Labor isn't *just* about efficiency anymore. It's about raw economic arbitrage. Read Case Study

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What Boards Often Misread as AI Progress I’ve seen boards feel good about AI progress when what they really had was activity. Across manufacturing and CPG environments I’ve worked in, the warning signs were consistent. Plenty of pilots. Plenty of demos. Very little clarity. Progress stalled when: • Ownership was implied • Value was discussed but not measured • Risk was assumed to belong to someone else AI progress is not motion. It is decisions getting better over time. When boards focus on outcomes, ownership, and durability, momentum follows. When they don’t, activity fills the gap. #AIGovernance #BoardLeadership #ExecutiveOversight #DigitalStrategy

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